Amazon Lightsail
The fastest path from AI concept to production with predictable cloud infrastructure.
Improve your ML models by identifying and fixing the data that matters.
Aquarium Learning represents a critical shift in the 2026 MLOps landscape, focusing on 'Data-Centric AI' rather than model-centric iteration. Built by former autonomous vehicle engineers, the platform addresses the 'needle in a haystack' problem within massive unstructured datasets (images, video, and text). Its technical architecture revolves around embedding-based visualization, allowing ML teams to project high-dimensional model activations into a 2D/3D space to identify clusters of model failures. Following its acquisition by Scale AI, the tool has been deeply integrated into the Scale Data Engine, serving as the primary intelligence layer for identifying edge cases and directing labeling resources efficiently. In 2026, Aquarium is positioned as a high-fidelity data debugger that bridges the gap between raw data collection and model training, specifically optimized for high-stakes domains like autonomous systems, robotics, and generative AI safety. It provides a specialized UI for cross-functional teams to collaborate on dataset curation, ensuring that training sets are balanced and that rare but critical failure modes are addressed before deployment.
Uses dimensionality reduction to visualize how a model 'sees' data, highlighting regions where model performance is consistently poor.
The fastest path from AI concept to production with predictable cloud infrastructure.
The open-source multi-modal data labeling platform for high-performance AI training and RLHF.
Scalable, Kubernetes-native Hyperparameter Tuning and Neural Architecture Search for production-grade ML.
The enterprise-grade MLOps platform for automating the deployment, management, and scaling of machine learning models.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Algorithms that automatically surface subsets of data where the model disagrees most significantly with ground truth.
Directly compare the performance of two model versions on the same data slices to prevent regressions.
Technical filtering engine allowing users to query data based on complex metadata combinations (e.g., 'nighttime + rain + high_speed').
Programmatic selection of the most informative data points for labeling using uncertainty sampling.
Query your dataset using natural language or image-to-image similarity to find similar edge cases.
Statistical analysis of live production data vs. training data distributions.
Identifying why a vehicle fails to detect pedestrians specifically at dusk.
Registry Updated:2/7/2026
Send blurred samples for labeling.
Finding rare pathology examples in a massive dataset of normal X-rays.
Fixing inconsistent moderation of evolving internet slang.